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To build a web research AI agent, connect a model to a search tool, have it retrieve and inspect relevant pages, and require it to link important claims to the evidence. Search must be enabled in the agent or API configuration: asking for web access in a prompt does not grant it. You can use a hosted search tool or connect an external search API as a custom tool; the latter gives you more control but leaves more retrieval and citation plumbing to your application.
How a web research agent works
A research agent combines a model that plans and synthesizes with a retrieval tool that supplies current web material. The model is not itself the search index. A useful workflow separates the question, retrieval, evidence inspection, and answer so that the final response can be traced to source pages.
- Define the request: capture the research question, scope, freshness requirements, preferred source types, and desired answer format.
- Retrieve: let the model decide whether to call search, or have your application call it using an explicit query plan.
- Inspect evidence: open relevant pages where needed, check context and authority, and retain dates and geographic scope for changeable facts.
- Map claims to sources: connect each material answer claim to the page that supports it.
- Respond: show source links and identify unresolved points rather than inventing citations.
For deep-research requests, provide the API with a fully formed user request. The Responses API deep research documentation notes that it does not perform the clarification and prompt-rewriting steps described for ChatGPT Deep Research. OpenAI’s deep research guide also documents a maximum tool-call setting to constrain cost and latency, and recommends background mode for long-running requests.
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With OpenAI’s Responses API, include the built-in web_search tool in the request. The documented minimal shape is:
#1 Best Overall
const response = await client.responses.create({
model: process.env.MODEL_NAME,
tools: [{ type: "web_search" }],
input: "Research the question. Use authoritative sources and cite every material claim."
});This is a request-shape example, not a guarantee that a particular model or SDK version supports every option. Check the current Responses API web-search guide for supported model names, configuration, and output details.
In the Agents API, configure the search tool in the agent’s tools array. The documented settings include live for live internet access, cached for saved web content, and disabled to turn search off. The mode determines what kind of access the agent has; choose it to match the freshness needs of the task. The guide is explicit: leaving the search tool out disables built-in search, and a prompt cannot turn it on. See the Agents JS web-search guide for the current configuration details.
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Choose hosted search or a custom search API
A hosted tool is a shorter path to retrieval and can return source citation annotations with the model response. A custom search API gives your application direct control over provider-specific query options and result handling, but you must implement normalization, retries, source tracking, and citation construction. The available documentation does not establish that one provider is more accurate, faster, cheaper, or more complete than another.
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| Decision area | What to check |
|---|---|
| Freshness | Whether access is live or cached, and which recency controls are available. |
| Evidence returned | Whether results include titles, URLs, snippets, dates, source metadata, and page extraction. |
| Citations | Whether annotations are returned automatically or your application must assemble links. |
| Control and portability | Domain restrictions, ranking controls, provider switching, and ownership of the tool schema. |
| Operational burden | Credential handling, rate limits, retries, logging, error handling, and cost controls. |
Do not choose solely by an assumed quality or price advantage: a comparable provider benchmark or current provider pricing comparison is not established here. OpenAI’s tools guide describes built-in tools, function calling, programmatic tool calling, and remote MCP as integration patterns. For a custom search provider, expose a clear function schema with fields such as query, domain restrictions, recency, and result count, then pass normalized results to the model.
Rank #3
Build a reliable custom-search loop
Keep the retrieval interface standardized so the agent can use it predictably and the implementation can be changed without rewriting the research instructions. A practical loop is:
question + constraints
↓
query planning (one or more targeted searches)
↓
search API tool call
↓
result normalization (title, URL, snippet, date, provider metadata)
↓
page inspection / evidence extraction
↓
claim-to-source mapping
↓
answer with linked citations + explicit unresolved pointsThis is an engineering pattern, not a vendor-prescribed architecture. Keep the stages in one process for a simple agent. Split research and writing into separate agents only when the work requires different instructions, tools, or permissions. OpenAI’s practical guide to building agents covers data, action, and orchestration tools, and describes one agent serving as a tool for another.
Normalize results and inspect pages
Store each result’s title, URL, snippet, date when available, and provider metadata in a consistent structure. Treat search results as a discovery layer, not automatically as verified evidence. For consequential answers, inspect the underlying pages and compare claims across primary sources. Retain publication or update dates and geography for facts that can change or depend on location.
Preserve citations through the answer
The Responses API can return a web_search_call output item and message annotations containing cited URL information. Use those annotations or your own claim-to-source mapping to make links visible in the final interface. A citation should support the claim it accompanies; if retrieval does not find a supporting page, say so instead of fabricating a source.
If the agent’s task includes images, the OpenAI Agents JS tools guide explains that image search can expose raw result URLs and metadata on the web search call item. This is an optional capability, not a requirement for text-only research. The guide also describes how its JavaScript/TypeScript-friendly helper options map to Responses API tool payloads: Agents JS web-search guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep research tools read-only and bounded
Search is a data tool: it retrieves information. Sending a message or writing to a database is an external action and should be treated separately. Keep the research agent read-only unless the user explicitly requests an action and the application has designed appropriate controls.
Log the initial question, search queries, tool calls, retrieved URLs, timestamps, and the mapping from answer claims to sources. Set tool-call, time, and cost budgets for longer tasks, and return a bounded answer if a limit is reached. The Deep Research API guide identifies max_tool_calls as its main control for limiting tool calls, cost, and latency, and recommends background mode for long-running research. Its qualitative note that deep research may take tens of minutes is planning guidance, not a performance benchmark.
The Tool Desk
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- Confirm current endpoint names, model support, SDK syntax, tool settings, and response fields in the documentation for the version you deploy.
- Test whether the configured mode provides the freshness your task requires, and verify how missing or failed searches are represented.
- Check that citations remain attached to the claims they support when answers are formatted or transformed by your application.
- Decide how the agent handles conflicting sources, stale pages, and queries with no adequate evidence.
- Set and monitor limits for tool calls, runtime, and cost; define what the agent should return when it reaches a limit.
These checks matter because provider documentation and interfaces can change. The documented examples describe available patterns, not a guarantee that every request returns identical results.
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